Prostate cancer is the most common cancer in men, and has the greatest estimated heritable risk of all common cancers. Despite this, the heritable component of familial prostate cancer has proven complex and the underlying genes have remained largely elusive. Common, small-effect variants identified through genome wide association studies of prevalent prostate cancer do not explain observed familial clustering. Numerous segregation analyses have consistently demonstrated that familial clustering of prostate cancer is best explained by the inheritance of uncommon, large-effect variants. Familial prostate cancer has an earlier age of onset, with risk of more advanced disease at a late diagnosis. While Mendelian forms of other common cancers are known, greater complexity is now believed to underlie familial prostate cancer. After two decades of effort using linkage approaches, current knowledge remains remarkably limited. This motivates our study to elucidate the genetics of familial prostate cancer with a unique study design that accommodates scenarios of genetic heterogeneity, phenocopies, epistasis, and incomplete penetrance. We hypothesize that men who develop prostate cancer and who have a family history of the disease inherit uncommon genetic risk variants in the setting of complex heritability. Such genetic alterations could span a range of effect sizes and frequency, from recurrent, moderate-effect (reduced penetrance) risk variants, to rare large-effect mutations. Our aims will identify potential causative deleterious genetic variants carried by familial cases. To better delineate the etiology of the disease, we will identify the underlying molecular pathways. We will also assess which of the identified prostate cancer genes predispose to prostate cancer at an earlier age, later stage, or greater Gleason score at diagnosis. As in other familial cancers, men with a concerning family history of prostate cancer could benefit from genetic testing, particularly in the era of personalized medicine. Our study will significantly advance the field and further develop predictive models to guide clinical care.